Despite extensive research in handwriting recognition, satisfactory recognition accuracy still remains elusive. This is particularly true for unconstrained handwriting. Unconstrained handwriting occurs, for example, in handwritten recognition systems that are used to transcribe notes. For this type of unconstrained handwriting, handwriting recognition applications have to deal with users who are free to use any combination of printing and cursive writing. Additionally, people taking notes often write hurriedly, often write at a slant, and may not complete certain characters that should be completed. Thus, unconstrained handwriting recognition is quite hard.
Another reason for the lack of satisfactory recognition accuracy is the tremendous variability that exists between writing styles of individuals. Since one cannot know in advance the style of writing that an individual may have, one must either attempt to make a “universal” recognizer that can recognize any writing style, or make a system that can adapt to the particular style of a writer. Both systems exist and each has its own benefits and detriments. For example, universal recognizers tend to work better in locations where a large number of people use the recognition system, while adaptive recognizers tend to work well when only a single person uses the recognition system.
A detriment to both of these systems is that they are initially designed to perform a fixed set of functions. For instance, if a vendor has a particular recognizer, it is generally impractical or impossible to add any type of adaptive functionality to the recognizer. Moreover, if a system is adaptive, there is generally no way to use a different recognition engine with the system. Finally, adaptive learning techniques still deal with inherent imperfections in a handwritten recognizer. There are few techniques that help to correct errors caused by the statistical structure of handwriting recognizers. For example, if a person consistently writes the word “cat,” a handwriting recognizer might consistently transcribe this handwritten word into “cut.” This is accurate in a statistical sense, as the handwriting recognizer is always outputting the same word, but is an inaccurate transcription of the handwritten word. There are relatively few systems that can correct this error.
There is, therefore, a need for techniques that allow adaptive learning for currently existing handwriting recognition systems and that use the statistical structure of handwriting recognizers to correct transcription errors.